import numpy as np 
import pandas as pd

%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

import seaborn as sns

data = pd.read_csv("ai4i2020.csv")
data.replace("?",np.nan,inplace=True)
for column in data.columns:
    try:
        data[column] = data[column].astype(float)
    except:
        pass
data.drop(['UDI', 'Product ID'],axis=1,inplace=True)
data['Machine failure']=0
data['Machine failure'][data['TWF']==1]=1
data['Machine failure'][data['HDF']==1]=2
data['Machine failure'][data['PWF']==1]=3
data['Machine failure'][data['OSF']==1]=4
data['Machine failure'][data['RNF']==1]=5
data.drop(['TWF','HDF','PWF','OSF','RNF'],axis=1,inplace=True)
data['Power'] = data['Rotational speed [rpm]'] * data['Torque [Nm]']
data['Temperature difference'] = data['Process temperature [K]'] - data['Air temperature [K]']
data = data[[
    'Machine failure',
    'Type',
    'Air temperature [K]',
    'Process temperature [K]',
    'Rotational speed [rpm]',
    'Torque [Nm]',
    'Tool wear [min]',
    'Power',
    'Temperature difference'
]]
data.describe(include='all')
display(data)

	Machine failure	Type	Air temperature [K]	Process temperature [K]	Rotational speed [rpm]	Torque [Nm]	Tool wear [min]	Power	Temperature difference
0	0	M	298.1	308.6	1551.0	42.8	0.0	66382.8	10.5
1	0	L	298.2	308.7	1408.0	46.3	3.0	65190.4	10.5
2	0	L	298.1	308.5	1498.0	49.4	5.0	74001.2	10.4
3	0	L	298.2	308.6	1433.0	39.5	7.0	56603.5	10.4
4	0	L	298.2	308.7	1408.0	40.0	9.0	56320.0	10.5
...	...	...	...	...	...	...	...	...	...
9995	0	M	298.8	308.4	1604.0	29.5	14.0	47318.0	9.6
9996	0	H	298.9	308.4	1632.0	31.8	17.0	51897.6	9.5
9997	0	M	299.0	308.6	1645.0	33.4	22.0	54943.0	9.6
9998	0	H	299.0	308.7	1408.0	48.5	25.0	68288.0	9.7
9999	0	M	299.0	308.7	1500.0	40.2	30.0	60300.0	9.7

10000 rows × 9 columns

data.head()

	Machine failure	Type	Air temperature [K]	Process temperature [K]	Rotational speed [rpm]	Torque [Nm]	Tool wear [min]	Power	Temperature difference
0	0	M	298.1	308.6	1551.0	42.8	0.0	66382.8	10.5
1	0	L	298.2	308.7	1408.0	46.3	3.0	65190.4	10.5
2	0	L	298.1	308.5	1498.0	49.4	5.0	74001.2	10.4
3	0	L	298.2	308.6	1433.0	39.5	7.0	56603.5	10.4
4	0	L	298.2	308.7	1408.0	40.0	9.0	56320.0	10.5

data.shape

(10000, 9)

from ydata_profiling import ProfileReport

profile = ProfileReport(data, title="Pandas Profiling Report")

profile

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data.describe().T

	count	mean	std	min	25%	50%	75%	max
Machine failure	10000.0	0.09900	0.561988	0.0	0.0	0.0	0.00	5.0
Air temperature [K]	10000.0	300.00493	2.000259	295.3	298.3	300.1	301.50	304.5
Process temperature [K]	10000.0	310.00556	1.483734	305.7	308.8	310.1	311.10	313.8
Rotational speed [rpm]	10000.0	1538.77610	179.284096	1168.0	1423.0	1503.0	1612.00	2886.0
Torque [Nm]	10000.0	39.98691	9.968934	3.8	33.2	40.1	46.80	76.6
Tool wear [min]	10000.0	107.95100	63.654147	0.0	53.0	108.0	162.00	253.0
Power	10000.0	59967.14704	10193.093881	10966.8	53105.4	59883.9	66873.75	99980.4
Temperature difference	10000.0	10.00063	1.001094	7.6	9.3	9.8	11.00	12.1

data.select_dtypes(include=['object']).describe().T

	count	unique	top	freq
Type	10000	3	L	6000

data.columns

Index(['Machine failure', 'Type', 'Air temperature [K]',
       'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]',
       'Tool wear [min]', 'Power', 'Temperature difference'],
      dtype='object')

num_cols = ['Air temperature [K]', 'Process temperature [K]',
            'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']
cat_cols = ['Type', 'Failure type']
label = 'Machine failure'

data['Air temperature [K]'][data['Air temperature [K]'].isna().astype(int) == 1]

Series([], Name: Air temperature [K], dtype: float64)

plt.figure(figsize=(12, 12))
for i, col in enumerate(num_cols):
    plt.subplot(3, 2, i+1)
    sns.histplot(data, x=col, kde=True, alpha=0.2, color='red', bins=15)
    plt.title(col)
plt.suptitle("Data Distributions", fontsize=15)
plt.tight_layout()
plt.show()

plt.figure(figsize=(10, 7))
for i, col in enumerate(num_cols):
    plt.subplot(2, 3, i+1)
    sns.rugplot(data, x=col, hue=label, height=0.1)
    sns.boxplot(data, x=col, width=0.25)
plt.suptitle("Data Distributions")
plt.tight_layout()
plt.show()

plt.figure(figsize=(10, 7))
for i, col in enumerate(num_cols):
    plt.subplot(2, 3, i+1)
    sns.boxplot(data, x=label, y=col, width=0.5)
plt.suptitle("Data Distribution in Relation to Machine Failure")
plt.tight_layout()
plt.show()

#plt.figure(figsize = (6,6))
# , square = True, annot = True, cmap = 'Blues', linewidths = 0.5)
sns.heatmap(data[num_cols].corr(), annot=True, fmt=".2f")
plt.title("Heatmap Analysis")
plt.show()

data[num_cols].corr()

	Air temperature [K]	Process temperature [K]	Rotational speed [rpm]	Torque [Nm]	Tool wear [min]
Air temperature [K]	1.000000	0.876107	0.022670	-0.013778	0.013853
Process temperature [K]	0.876107	1.000000	0.019277	-0.014061	0.013488
Rotational speed [rpm]	0.022670	0.019277	1.000000	-0.875027	0.000223
Torque [Nm]	-0.013778	-0.014061	-0.875027	1.000000	-0.003093
Tool wear [min]	0.013853	0.013488	0.000223	-0.003093	1.000000

data.plot.hexbin(x='Air temperature [K]', y='Process temperature [K]',
                 gridsize=20, cmap='Purples', figsize=(5, 4))
plt.title("Hexbin Plot Between Process Temperature and Air Temperature")
plt.show()

data.plot.hexbin(x='Rotational speed [rpm]', y='Torque [Nm]',
                 gridsize=30, cmap='Purples', figsize=(5, 4))
plt.title("Hexbin Plot Between Torque and Rotational speed")
plt.show()

type_machine_failure = data[['Type', 'Machine failure']].pivot_table(index='Type', columns='Machine failure', aggfunc= lambda x: len(x), margins = True)
print(type_machine_failure)
plt.figure(figsize=(6,6))
sns.heatmap(type_machine_failure, annot=True, fmt='g', cmap='Blues', cbar=False, linewidths=0.5)
plt.title("Type vs Machine Failure")
plt.show()

Machine failure     0   1    2   3   4   5    All
Type                                             
H                 979   6    8   4   2   4   1003
L                5757  24   68  51  87  13   6000
M                2916  12   30  28   9   2   2997
All              9652  42  106  83  98  19  10000

from pycaret.classification import *
s = setup(data, target = 'Machine failure', session_id = 42, data_split_stratify=True)

best_model = compare_models(sort = 'AUC')

Initiated	. . . . . . . . . . . . . . . . . .	12:06:02
Status	. . . . . . . . . . . . . . . . . .	Loading Dependencies
Estimator	. . . . . . . . . . . . . . . . . .	Compiling Library

plt.figure(figsize = (4,3))
plot_model(best_model, plot = 'confusion_matrix')

plt.figure(figsize = (5,4))
plot_model(best_model, plot = 'auc')

plt.figure(figsize = (5, 4))
plot_model(best_model, plot = 'learning')

plot_model(best_model, plot = 'feature')

save_model(best_model, "ai4i2020_pycaret_model")

Transformation Pipeline and Model Successfully Saved

(Pipeline(memory=Memory(location=None),
          steps=[('numerical_imputer',
                  TransformerWrapper(exclude=None,
                                     include=['Air temperature [K]',
                                              'Process temperature [K]',
                                              'Rotational speed [rpm]',
                                              'Torque [Nm]', 'Tool wear [min]',
                                              'Power',
                                              'Temperature difference'],
                                     transformer=SimpleImputer(add_indicator=False,
                                                               copy=True,
                                                               fill_value=None,
                                                               keep_empty_features=False,
                                                               missing_values=n...
                  RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                         class_weight=None, criterion='gini',
                                         max_depth=None, max_features='sqrt',
                                         max_leaf_nodes=None, max_samples=None,
                                         min_impurity_decrease=0.0,
                                         min_samples_leaf=1, min_samples_split=2,
                                         min_weight_fraction_leaf=0.0,
                                         monotonic_cst=None, n_estimators=100,
                                         n_jobs=-1, oob_score=False,
                                         random_state=42, verbose=0,
                                         warm_start=False))],
          verbose=False),
 'ai4i2020_pycaret_model.pkl')

#plot_model(best_model, plot = 'calibration') # NOT WORKING

calibrated_model = calibrate_model(best_model)

#plot_model(calibrated_model, plot = 'calibration') # NOT WORKING

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[65], line 1
----> 1 plot_model(calibrated_model, plot = 'calibration') # NOT WORKING

File c:\ProgramData\anaconda3\Lib\site-packages\pycaret\utils\generic.py:964, in check_if_global_is_not_none.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    962     if globals_d[name] is None:
    963         raise ValueError(message)
--> 964 return func(*args, **kwargs)

File c:\ProgramData\anaconda3\Lib\site-packages\pycaret\classification\functional.py:1725, in plot_model(estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, verbose, display_format)
   1611 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
   1612 def plot_model(
   1613     estimator,
   (...)
   1622     display_format: Optional[str] = None,
   1623 ) -> Optional[str]:
   1624     """
   1625     This function analyzes the performance of a trained model on holdout set.
   1626     It may require re-training the model in certain cases.
   (...)
   1722 
   1723     """
-> 1725     return _CURRENT_EXPERIMENT.plot_model(
   1726         estimator=estimator,
   1727         plot=plot,
   1728         scale=scale,
   1729         save=save,
   1730         fold=fold,
   1731         fit_kwargs=fit_kwargs,
   1732         plot_kwargs=plot_kwargs,
   1733         groups=groups,
   1734         verbose=verbose,
   1735         display_format=display_format,
   1736     )

File c:\ProgramData\anaconda3\Lib\site-packages\pycaret\classification\oop.py:2071, in ClassificationExperiment.plot_model(self, estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, verbose, display_format)
   1957 def plot_model(
   1958     self,
   1959     estimator,
   (...)
   1968     display_format: Optional[str] = None,
   1969 ) -> Optional[str]:
   1970     """
   1971     This function analyzes the performance of a trained model on holdout set.
   1972     It may require re-training the model in certain cases.
   (...)
   2068 
   2069     """
-> 2071     return super().plot_model(
   2072         estimator=estimator,
   2073         plot=plot,
   2074         scale=scale,
   2075         save=save,
   2076         fold=fold,
   2077         fit_kwargs=fit_kwargs,
   2078         plot_kwargs=plot_kwargs,
   2079         groups=groups,
   2080         verbose=verbose,
   2081         display_format=display_format,
   2082     )

File c:\ProgramData\anaconda3\Lib\site-packages\pycaret\internal\pycaret_experiment\tabular_experiment.py:2045, in _TabularExperiment.plot_model(self, estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, feature_name, label, verbose, display_format)
   1933 def plot_model(
   1934     self,
   1935     estimator,
   (...)
   1946     display_format: Optional[str] = None,
   1947 ) -> Optional[str]:
   1948     """
   1949     This function takes a trained model object and returns a plot based on the
   1950     test / hold-out set. The process may require the model to be re-trained in
   (...)
   2043 
   2044     """
-> 2045     return self._plot_model(
   2046         estimator=estimator,
   2047         plot=plot,
   2048         scale=scale,
   2049         save=save,
   2050         fold=fold,
   2051         fit_kwargs=fit_kwargs,
   2052         plot_kwargs=plot_kwargs,
   2053         groups=groups,
   2054         feature_name=feature_name,
   2055         label=label,
   2056         verbose=verbose,
   2057         display_format=display_format,
   2058     )

File c:\ProgramData\anaconda3\Lib\site-packages\pycaret\internal\pycaret_experiment\tabular_experiment.py:427, in _TabularExperiment._plot_model(self, estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, feature_name, label, verbose, system, display, display_format)
    425 if self.is_multiclass:
    426     if plot in multiclass_not_available:
--> 427         raise ValueError(
    428             "Plot Not Available for multiclass problems. Please see docstring for list of available Plots."
    429         )
    431 # exception for CatBoost
    432 # if "CatBoostClassifier" in str(type(estimator)):
    433 #    raise ValueError(
   (...)
    436 
    437 # checking for auc plot
    438 if not hasattr(estimator, "predict_proba") and plot == "auc":

ValueError: Plot Not Available for multiclass problems. Please see docstring for list of available Plots.

automl()

CalibratedClassifierCV(cv=5, ensemble=True,
                       estimator=RandomForestClassifier(bootstrap=True,
                                                        ccp_alpha=0.0,
                                                        class_weight=None,
                                                        criterion='gini',
                                                        max_depth=None,
                                                        max_features='sqrt',
                                                        max_leaf_nodes=None,
                                                        max_samples=None,
                                                        min_impurity_decrease=0.0,
                                                        min_samples_leaf=1,
                                                        min_samples_split=2,
                                                        min_weight_fraction_leaf=0.0,
                                                        monotonic_cst=None,
                                                        n_estimators=100,
                                                        n_jobs=-1,
                                                        oob_score=False,
                                                        random_state=42,
                                                        verbose=0,
                                                        warm_start=False),
                       method='sigmoid', n_jobs=None)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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#create_app(best_model)